|
r-pocrm
|
public |
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
|
2025-03-25 |
|
r-pnn
|
public |
The program pnn implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets and should guess afterwards the group of any new observation. This neural network has the main advantage to begin generalization instantaneously even with a small set of known observations. It is delivered with four functions (learn, smooth, perf and guess) and a dataset. The functions are documented with examples and provided with unit tests.
|
2025-03-25 |
|
r-pnmtrem
|
public |
An R package for Probit-Normal Marginalized Transition Random Effects Models
|
2025-03-25 |
|
r-pmultinom
|
public |
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
|
2025-03-25 |
|
r-pmsampsize
|
public |
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
|
2025-03-25 |
|
r-pmr
|
public |
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
|
2025-03-25 |
|
r-pmmltransformations
|
public |
Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
|
2025-03-25 |
|
r-pmml
|
public |
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github("Zelazny7/isofor").
|
2025-03-25 |
|
r-pmcmr
|
public |
Note, that the 'PMCMR' package is superseded by the novel 'PMCMRplus' package. The 'PMCMRplus' package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time.
|
2025-03-25 |
|
r-plusser
|
public |
plusser provides an API interface to Google+ so that posts, profiles and pages can be automatically retrieved.
|
2025-03-25 |
|
r-pluscode
|
public |
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
|
2025-03-25 |
|
r-plus
|
public |
Efficient procedures for fitting an entire regression sequences with different model types.
|
2025-03-25 |
|
r-plsdof
|
public |
The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures. Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.
|
2025-03-25 |
|
r-plsdepot
|
public |
plsdepot contains different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus.
|
2025-03-25 |
|
r-plsbiplot1
|
public |
Principal Component Analysis (PCA) biplots, Covariance monoplots and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares (SPLS) biplots and Sparse Partial Least Squares for Generalized Linear Model (SPLS-GLM) biplots.
|
2025-03-25 |
|
r-plrmodels
|
public |
This package provides statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
|
2025-03-25 |
|
r-plrasch
|
public |
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
|
2025-03-25 |
|
r-plotwidgets
|
public |
Small self-contained plots for use in larger plots or to delegate plotting in other functions. Also contains a number of alternative color palettes and HSL color space based tools to modify colors or palettes.
|
2025-03-25 |
|
r-plotscale
|
public |
Figures rendered on graphics devices are usually rescaled to fit pre-determined device dimensions. 'plotscale' implements the reverse: desired plot dimensions are specified and device dimensions are calculated to accommodate marginal material, giving consistent proportions for plot elements. Default methods support grid graphics such as lattice and ggplot. See "example('devsize')" and "vignette('plotscale')".
|
2025-03-25 |
|
r-plotrix
|
public |
Lots of plots, various labeling, axis and color scaling functions.
|
2025-03-25 |
|
r-plotregionhighlighter
|
public |
Creates an envelope around a set of plotted points. The envelope is compact with a boundary that is continuous, smooth and convex. Each point is represented as a circle and the circles and connecting lines are the solution to the multiple pulley problem. This method can be used to highlight regions in a two-dimensional space.
|
2025-03-25 |
|
r-plotpc
|
public |
Plot principal component histograms around a bivariate scatter plot.
|
2025-03-25 |
|
r-plotmcmc
|
public |
Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the 'coda' and 'lattice' packages, and make it easy to adjust graphical details.
|
2025-03-25 |
|
r-plot.matrix
|
public |
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
|
2025-03-25 |
|
r-plotlygeoassets
|
public |
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
|
2025-03-25 |
|
r-plotfunctions
|
public |
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
|
2025-03-25 |
|
r-plotcontour
|
public |
This function plots a contour line with a user-defined probability and tightness of fit.
|
2025-03-25 |
|
r-plis
|
public |
PLIS is a multiple testing procedure for testing several groups of hypotheses. Linear dependency is expected from the hypotheses within the same group and is modeled by hidden Markov Models. It is noted that, for PLIS, a smaller p value does not necessarily imply more significance because of dependency among the hypotheses. A typical application of PLIS is to analyze genome wide association studies datasets, where SNPs from the same chromosome are treated as a group and exhibit strong linear genomic dependency.
|
2025-03-25 |
|
r-plde
|
public |
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
|
2025-03-25 |
|
r-plaqr
|
public |
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression. Intuitive functions for fitting and plotting partially linear additive quantile regression models. Uses and works with functions from the 'quantreg' package.
|
2025-03-25 |
|
r-plantecophys
|
public |
Coupled leaf gas exchange model, A-Ci curve simulation and fitting, Ball-Berry stomatal conductance models, leaf energy balance using Penman-Monteith, Cowan-Farquhar optimization, humidity unit conversions. See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
|
2025-03-25 |
|
r-planets
|
public |
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
|
2025-03-25 |
|
r-planesmuestra
|
public |
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
|
2025-03-25 |
|
r-plan
|
public |
Supports the creation of 'burndown' charts and 'gantt' diagrams.
|
2025-03-25 |
|
r-pla
|
public |
Parallel Line Assays: Completely randomized design, Randomized Block design, and Latin squares design. Balanced data are fitted as described in the Ph.Eur. In the presence of missing values complete data analysis can be performed (with computation of Fieller's confidence intervals for the estimated potency), or imputation of values can be applied. The package contains a script such that a pdf-document with a report of an analysis of an assay can be produced from an input file with data of the assay. Here no knowledge of R is needed by the user.
|
2025-03-25 |
|
r-pksea
|
public |
A tool for inferring kinase activity changes from phosphoproteomics data. 'pKSEA' uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). 'pKSEA' then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. 'pKSEA' contains default databases of kinase-substrate predictions from 'NetworKIN' (NetworKINPred_db) <http://networkin.info> Horn, et. al (2014) <doi:10.1038/nmeth.2968> and of known kinase-substrate links from 'PhosphoSitePlus' (KSEAdb) <https://www.phosphosite.org/> Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
|
2025-03-25 |
|
r-pkpdmodels
|
public |
Provides functions to evaluate common pharmacokinetic/pharmacodynamic models and their gradients.
|
2025-03-25 |
|
r-pkmon
|
public |
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
|
2025-03-25 |
|
r-pkgkitten
|
public |
Provides a function kitten() which creates cute little packages which pass R package checks. This sets it apart from package.skeleton() which it calls, and which leaves imperfect files behind. As this is not exactly helpful for beginners, kitten() offers an alternative.
|
2025-03-25 |
|
r-pkgcond
|
public |
This provides utilities for creating classed error and warning conditions based on where the error originated.
|
2025-03-25 |
|
r-pkconverter
|
public |
Pharmacokinetics is the study of drug absorption, distribution, metabolism, and excretion. The pharmacokinetics model explains that how the drug concentration change as the drug moves through the different compartments of the body. For pharmacokinetic modeling and analysis, it is essential to understand the basic pharmacokinetic parameters. All parameters are considered, but only some of parameters are used in the model. Therefore, we need to convert the estimated parameters to the other parameters after fitting the specific pharmacokinetic model. This package is developed to help this converting work. For more detailed explanation of pharmacokinetic parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001"; "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; "Mould and Upton" (2012) <doi:10.1038/psp.2012.4>; "Mould and Upton" (2013) <doi:10.1038/psp.2013.14>.
|
2025-03-25 |
|
r-pk
|
public |
Estimation of pharmacokinetic parameters using non-compartmental theory.
|
2025-03-25 |
|
r-pixmap
|
public |
Functions for import, export, plotting and other manipulations of bitmapped images.
|
2025-03-25 |
|
r-pixels
|
public |
Provides tools to show and draw image pixels using 'HTML' widgets and 'Shiny' applications. It can be used to visualize the 'MNIST' dataset for handwritten digit recognition or to create new image recognition datasets.
|
2025-03-25 |
|
r-pivotaltrackr
|
public |
'Pivotal Tracker' <https://www.pivotaltracker.com> is a project management software-as-a-service that provides a REST API. This package provides an R interface to that API, allowing you to query it and work with its responses.
|
2025-03-25 |
|
r-pips
|
public |
Generate Predicted Interval Plots. Simulate and plot confidence intervals of an effect estimate given observed data and a hypothesis about the distribution of future data.
|
2025-03-25 |
|
r-piper
|
public |
Provides various styles of function chaining methods: Pipe operator, Pipe object, and pipeline function, each representing a distinct pipeline model yet sharing almost a common set of features: A value can be piped to the first unnamed argument of a function and to dot symbol in an enclosed expression. The syntax is designed to make the pipeline more readable and friendly to a wide range of operations.
|
2025-03-25 |
|
r-pipeliner
|
public |
A framework for defining 'pipelines' of functions for applying data transformations, model estimation and inverse-transformations, resulting in predicted value generation (or model-scoring) functions that automatically apply the entire pipeline of functions required to go from input to predicted output.
|
2025-03-25 |
|
r-pipegs
|
public |
Code for various permutation p-values estimation methods for gene set test. The description of corresponding methods can be found in the dissertation of Yu He(2016) "Efficient permutation P-value estimation for gene set tests" <https://searchworks.stanford.edu/view/11849351>. One of the methods also corresponds to the paper "Permutation p-value approximation via generalized Stolarsky invariance" <arXiv:1603.02757>.
|
2025-03-25 |
|
r-pipefittr
|
public |
To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
|
2025-03-25 |
